Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with D...

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Autores principales:	Zan Li, Madeline Fields, Fedor Panov, Saadi Ghatan, Bülent Yener, Lara Marcuse
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	intracranial and scalp EEG deep neural networks LSTM (long short term memory networks) seizure lateralization seizure prediction convolutional neural networks Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/2dc462efda4a47ea9a89ff1cecc30ba0
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spelling	oai:doaj.org-article:2dc462efda4a47ea9a89ff1cecc30ba02021-11-11T12:47:30ZDeep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures1664-229510.3389/fneur.2021.705119https://doaj.org/article/2dc462efda4a47ea9a89ff1cecc30ba02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fneur.2021.705119/fullhttps://doaj.org/toc/1664-2295In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.Zan LiMadeline FieldsFedor PanovSaadi GhatanBülent YenerLara MarcuseFrontiers Media S.A.articleintracranial and scalp EEGdeep neural networksLSTM (long short term memory networks)seizure lateralizationseizure predictionconvolutional neural networksNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Neurology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intracranial and scalp EEG deep neural networks LSTM (long short term memory networks) seizure lateralization seizure prediction convolutional neural networks Neurology. Diseases of the nervous system RC346-429
spellingShingle	intracranial and scalp EEG deep neural networks LSTM (long short term memory networks) seizure lateralization seizure prediction convolutional neural networks Neurology. Diseases of the nervous system RC346-429 Zan Li Madeline Fields Fedor Panov Saadi Ghatan Bülent Yener Lara Marcuse Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
description	In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.
format	article
author	Zan Li Madeline Fields Fedor Panov Saadi Ghatan Bülent Yener Lara Marcuse
author_facet	Zan Li Madeline Fields Fedor Panov Saadi Ghatan Bülent Yener Lara Marcuse
author_sort	Zan Li
title	Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
title_short	Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
title_full	Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
title_fullStr	Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
title_full_unstemmed	Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
title_sort	deep learning of simultaneous intracranial and scalp eeg for prediction, detection, and lateralization of mesial temporal lobe seizures
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/2dc462efda4a47ea9a89ff1cecc30ba0
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Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures

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